Abstract:
Distributed manufacturing involving heterogeneous factories presents significant challenges to enterprises. Furthermore, the need to prioritize various jobs based on orde...View moreMetadata
Abstract:
Distributed manufacturing involving heterogeneous factories presents significant challenges to enterprises. Furthermore, the need to prioritize various jobs based on order urgency and customer importance further complicates the scheduling process. Consequently, this study addresses the practical issue by tackling the distributed heterogeneous hybrid flow shop scheduling problem with multiple priorities of jobs (DHHFSP-MPJ). The primary objective is to simultaneously minimize the total weighted tardiness and total energy consumption. To solve DHHFSP-MPJ, a double deep Q-network-based co- evolution (D2QCE) is developed with four features: i) The global and local searches are allocated into two populations to balance computational resources; ii) A hybrid heuristic strategy is proposed to obtain an initialized population with great convergence and diversity; iii) Four knowledge-based neighborhood structures are proposed to accelerate converging. Next, the double deep Q-Network is applied to learn operator selection; and iv) An energy-efficient strategy is presented to save energy. To verify the effectiveness of D2QCE, five state-of-the-art algorithms are compared on 20 instances and a real-world case. The results of numerical experiments indicate that: i) The D2QN can learn fast by only consuming a few computation resources and can select the best operator. ii) Combining D2QN and co- evolution can vastly improve the performance of evolutionary algorithms for solving distributed shop scheduling. iii) The proposed D2QCE has better performance than state-of-the-arts for DHHFSP-MPJ. Note to Practitioners—This paper is inspired by a real-world problem encountered in blanking workshop systems within the manufacturing of large engineering equipment. In this practical scenario, jobs come with varying priorities and distinct due dates. Balancing these priority and due date constraints while efficiently scheduling a considerable volume of jobs to enhance enterprise profitability ...
Published in: IEEE Transactions on Automation Science and Engineering ( Volume: 21, Issue: 4, October 2024)